Evaluating Explanations: How much do explanations from the teacher aid
students?
- URL: http://arxiv.org/abs/2012.00893v1
- Date: Tue, 1 Dec 2020 23:40:21 GMT
- Title: Evaluating Explanations: How much do explanations from the teacher aid
students?
- Authors: Danish Pruthi, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins,
Zachary C. Lipton, Graham Neubig, William W. Cohen
- Abstract summary: We formalize the value of explanations using a student-teacher paradigm that measures the extent to which explanations improve student models in learning.
Unlike many prior proposals to evaluate explanations, our approach cannot be easily gamed, enabling principled, scalable, and automatic evaluation of attributions.
- Score: 103.05037537415811
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: While many methods purport to explain predictions by highlighting salient
features, what precise aims these explanations serve and how to evaluate their
utility are often unstated. In this work, we formalize the value of
explanations using a student-teacher paradigm that measures the extent to which
explanations improve student models in learning to simulate the teacher model
on unseen examples for which explanations are unavailable. Student models
incorporate explanations in training (but not prediction) procedures. Unlike
many prior proposals to evaluate explanations, our approach cannot be easily
gamed, enabling principled, scalable, and automatic evaluation of attributions.
Using our framework, we compare multiple attribution methods and observe
consistent and quantitative differences amongst them across multiple learning
strategies.
Related papers
- Selective Explanations [14.312717332216073]
A machine learning model is trained to predict feature attribution scores with only one inference.
Despite their efficiency, amortized explainers can produce inaccurate predictions and misleading explanations.
We propose selective explanations, a novel feature attribution method that detects when amortized explainers generate low-quality explanations.
arXiv Detail & Related papers (2024-05-29T23:08:31Z) - Explainability for Machine Learning Models: From Data Adaptability to
User Perception [0.8702432681310401]
This thesis explores the generation of local explanations for already deployed machine learning models.
It aims to identify optimal conditions for producing meaningful explanations considering both data and user requirements.
arXiv Detail & Related papers (2024-02-16T18:44:37Z) - Evaluating the Utility of Model Explanations for Model Development [54.23538543168767]
We evaluate whether explanations can improve human decision-making in practical scenarios of machine learning model development.
To our surprise, we did not find evidence of significant improvement on tasks when users were provided with any of the saliency maps.
These findings suggest caution regarding the usefulness and potential for misunderstanding in saliency-based explanations.
arXiv Detail & Related papers (2023-12-10T23:13:23Z) - Counterfactuals of Counterfactuals: a back-translation-inspired approach
to analyse counterfactual editors [3.4253416336476246]
We focus on the analysis of counterfactual, contrastive explanations.
We propose a new back translation-inspired evaluation methodology.
We show that by iteratively feeding the counterfactual to the explainer we can obtain valuable insights into the behaviour of both the predictor and the explainer models.
arXiv Detail & Related papers (2023-05-26T16:04:28Z) - Learning with Explanation Constraints [91.23736536228485]
We provide a learning theoretic framework to analyze how explanations can improve the learning of our models.
We demonstrate the benefits of our approach over a large array of synthetic and real-world experiments.
arXiv Detail & Related papers (2023-03-25T15:06:47Z) - Complementary Explanations for Effective In-Context Learning [77.83124315634386]
Large language models (LLMs) have exhibited remarkable capabilities in learning from explanations in prompts.
This work aims to better understand the mechanisms by which explanations are used for in-context learning.
arXiv Detail & Related papers (2022-11-25T04:40:47Z) - Learning to Scaffold: Optimizing Model Explanations for Teaching [74.25464914078826]
We train models on three natural language processing and computer vision tasks.
We find that students trained with explanations extracted with our framework are able to simulate the teacher significantly more effectively than ones produced with previous methods.
arXiv Detail & Related papers (2022-04-22T16:43:39Z) - Evaluations and Methods for Explanation through Robustness Analysis [117.7235152610957]
We establish a novel set of evaluation criteria for such feature based explanations by analysis.
We obtain new explanations that are loosely necessary and sufficient for a prediction.
We extend the explanation to extract the set of features that would move the current prediction to a target class.
arXiv Detail & Related papers (2020-05-31T05:52:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.